Analysis date: 2023-08-08

Depends on

CRC_Xenografts_Batch2_DataProcessing Script

load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")

TODO

Setup

Load libraries and functions

Analysis

DEP

Tyrosine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set5_normXenograft1, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway        pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.687622790
## 2:                                       ABC transporter disorders 0.082329317
## 3:                          ABC-family proteins mediated transport 0.082329317
## 4:                       ADP signalling through P2Y purinoceptor 1 0.470695971
## 5:                                           ALK mutants bind TKIs 0.199633700
## 6:               APC/C-mediated degradation of cell cycle proteins 0.006884729
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.8538066 0.06224904 -0.6652542 -0.8901041    1        6385
## 2: 0.3633962 0.22205605  0.9661017  1.2978731    1        5692
## 3: 0.3633962 0.22205605  0.9661017  1.2978731    1        5692
## 4: 0.7346950 0.07767986  0.6324772  0.9994649    2        6714
## 5: 0.5140811 0.13077714  0.7582498  1.1982157    2  1213,27436
## 6: 0.1853547 0.40701792  0.9702128  1.5331678    2    983,5692
data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set5_normXenograft1, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway       pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.92190476
## 2:                                       ABC transporter disorders 0.14095238
## 3:                          ABC-family proteins mediated transport 0.14095238
## 4:                       ADP signalling through P2Y purinoceptor 1 0.59756098
## 5:                                           ALK mutants bind TKIs 0.94757282
## 6:               APC/C-mediated degradation of cell cycle proteins 0.02022139
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.9630818 0.04716425  0.5466102  0.7322796    1        6385
## 2: 0.7474717 0.16197895  0.9364407  1.2545256    1        5692
## 3: 0.7474717 0.16197895  0.9364407  1.2545256    1        5692
## 4: 0.9070775 0.07078991  0.6035936  0.9539074    2        6714
## 5: 0.9767694 0.04678830 -0.3829787 -0.5812824    2  27436,1213
## 6: 0.3959715 0.35248786  0.9404255  1.4862298    2    983,5692
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set5_normXenograft1, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.7737374
## 2:                                       ABC transporter disorders 0.3393939
## 3:                          ABC-family proteins mediated transport 0.3393939
## 4:                       ADP signalling through P2Y purinoceptor 1 0.1475096
## 5:                                           ALK mutants bind TKIs 0.4731801
## 6:               APC/C-mediated degradation of cell cycle proteins 0.1264368
##         padj    log2err        ES       NES size leadingEdge
## 1: 0.8799526 0.05797548 0.6144068 0.8169759    1        6385
## 2: 0.6621871 0.10171390 0.8474576 1.1268634    1        5692
## 3: 0.6621871 0.10171390 0.8474576 1.1268634    1        5692
## 4: 0.6621871 0.15851411 0.8314748 1.2720413    2   6714,1432
## 5: 0.7686500 0.07977059 0.6654134 1.0179904    2  1213,27436
## 6: 0.6321336 0.17232434 0.8510638 1.3020099    2    983,5692

## Note: Row-scaling applied for this heatmap

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set5_normXenograft1, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.5928854
## 2:                                       ABC transporter disorders 0.9841897
## 3:                          ABC-family proteins mediated transport 0.9841897
## 4:                       ADP signalling through P2Y purinoceptor 1 0.6348774
## 5:                                           ALK mutants bind TKIs 0.7735849
## 6:               APC/C-mediated degradation of cell cycle proteins 0.2697548
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.8872970 0.06977925  0.7033898  0.9484105    1        6385
## 2: 0.9918233 0.04586203  0.5084746  0.6855979    1        5692
## 3: 0.9918233 0.04586203  0.5084746  0.6855979    1        5692
## 4: 0.9232023 0.08289621  0.5446809  0.8850280    2   1432,6714
## 5: 0.9918233 0.04641550 -0.5446809 -0.8108519    2  1213,27436
## 6: 0.7532999 0.13802224  0.7148006  1.1614481    2    983,5692
#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set5_normXenograft1, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

##                                                            pathway      pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.9866920
## 2:                                       ABC transporter disorders 0.5019011
## 3:                          ABC-family proteins mediated transport 0.5019011
## 4:                       ADP signalling through P2Y purinoceptor 1 0.7764706
## 5:                                           ALK mutants bind TKIs 0.3831933
## 6:               APC/C-mediated degradation of cell cycle proteins 0.3235294
##         padj    log2err         ES        NES size leadingEdge
## 1: 0.9950068 0.04397593 -0.5042373 -0.6796597    1        6385
## 2: 0.8781315 0.07627972 -0.7457627 -1.0052110    1        5692
## 3: 0.8781315 0.07627972 -0.7457627 -1.0052110    1        5692
## 4: 0.9316552 0.04929177  0.5182433  0.8067578    2   1432,6714
## 5: 0.8781315 0.08407456  0.6936170  1.0797651    2  1213,27436
## 6: 0.8781315 0.11724972 -0.7489362 -1.1611361    2    983,5692
#data_results <- get_df_long(dep)

Serine/Threonine all

Each condition vs ctrl

data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set5_normXenograft1, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                    pathway      pval      padj
## 1:                                  2-LTR circle formation 0.6282051 0.9910908
## 2:                               ABC transporter disorders 0.2564103 0.9910908
## 3:                  ABC-family proteins mediated transport 0.2583082 0.9910908
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.8569501 0.9910908
## 5:               AKT phosphorylates targets in the cytosol 0.9335347 0.9910908
## 6:                                   ALK mutants bind TKIs 0.3280423 0.9910908
##       log2err         ES        NES size leadingEdge
## 1: 0.07078991 -0.7104377 -0.9339203    1        3159
## 2: 0.12384217 -0.8754209 -1.1508023    1        5684
## 3: 0.10063339 -0.7991094 -1.1640850    2        5684
## 4: 0.03464102 -0.4660188 -0.7368427    3        5577
## 5: 0.03592087 -0.4712812 -0.6865285    2       84335
## 6: 0.17821987  0.4991540  1.0810484    4   6801,5573
## Warning:  we couldn't map to STRING 2% of your identifiers
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set5_normXenograft1, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                    pathway      pval      padj
## 1:                                  2-LTR circle formation 0.9960000 0.9998561
## 2:                               ABC transporter disorders 0.3127490 0.9443669
## 3:                  ABC-family proteins mediated transport 0.6971609 0.9751725
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.7311475 0.9751725
## 5:               AKT phosphorylates targets in the cytosol 0.3990536 0.9751725
## 6:                                   ALK mutants bind TKIs 0.7148760 0.9751725
##       log2err         ES        NES size    leadingEdge
## 1: 0.04586203  0.5050505  0.6656025    1           3159
## 2: 0.10592029 -0.8417508 -1.1215029    1           5684
## 3: 0.05132233 -0.5961474 -0.8847886    2           5684
## 4: 0.08528847  0.4324324  0.7756898    3      5576,5573
## 5: 0.07850290 -0.7079430 -1.0507131    2          84335
## 6: 0.09992770  0.3976311  0.7916561    4 6801,4869,5573
## Warning:  we couldn't map to STRING 6% of your identifiers

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set5_normXenograft1, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                    pathway      pval      padj
## 1:                                  2-LTR circle formation 0.6164659 1.0000000
## 2:                               ABC transporter disorders 0.2369478 0.9027377
## 3:                  ABC-family proteins mediated transport 0.1959565 0.9027377
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.1944444 0.9027377
## 5:               AKT phosphorylates targets in the cytosol 0.7231726 1.0000000
## 6:                                   ALK mutants bind TKIs 0.6550218 1.0000000
##       log2err         ES        NES size    leadingEdge
## 1: 0.06863256 -0.7037037 -0.9396615    1           3159
## 2: 0.12503337 -0.8787879 -1.1734529    1           5684
## 3: 0.12043337 -0.8291864 -1.2175548    2           5684
## 4: 0.18820415  0.6672297  1.2424457    3 5576,5573,5577
## 5: 0.04899541 -0.5800838 -0.8517793    2          84335
## 6: 0.10882013  0.4094755  0.8725818    4      6801,5573
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set5_normXenograft1, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                    pathway       pval      padj
## 1:                                  2-LTR circle formation 0.07768924 0.8676415
## 2:                               ABC transporter disorders 0.60358566 0.9661613
## 3:                  ABC-family proteins mediated transport 0.38157895 0.9661613
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.94736842 0.9827926
## 5:               AKT phosphorylates targets in the cytosol 0.01218565 0.6932841
## 6:                                   ALK mutants bind TKIs 0.72527473 0.9661613
##       log2err         ES        NES size          leadingEdge
## 1: 0.22798720  0.9612795  1.3013697    1                 3159
## 2: 0.06928365  0.6919192  0.9367127    1                 5684
## 3: 0.08312913  0.6930860  1.0695220    2              23,5684
## 4: 0.07182763 -0.3496622 -0.6500324    3       5573,5577,5576
## 5: 0.38073040 -0.9537873 -1.5623729    2                84335
## 6: 0.04319020  0.4331583  0.8193379    4 4869,5573,6801,27436
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set5_normXenograft1, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
##                                                    pathway      pval      padj
## 1:                                  2-LTR circle formation 0.1769384 0.9955648
## 2:                               ABC transporter disorders 0.4791252 0.9955648
## 3:                  ABC-family proteins mediated transport 0.2563238 0.9955648
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.5090909 0.9955648
## 5:               AKT phosphorylates targets in the cytosol 0.2738386 0.9955648
## 6:                                   ALK mutants bind TKIs 0.9284627 0.9955648
##       log2err         ES        NES size leadingEdge
## 1: 0.14641624 -0.9259259 -1.2217139    1        3159
## 2: 0.08108021 -0.7794613 -1.0284609    1        5684
## 3: 0.10797236 -0.7807757 -1.1745956    2     23,5684
## 4: 0.09255289  0.5214048  0.9310801    3   5577,5573
## 5: 0.12878871  0.7707644  1.1995212    2   84335,572
## 6: 0.03653149 -0.3447187 -0.6024988    4        4869

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.1                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] circlize_0.4.15        fastmatch_1.1-3        plyr_1.8.8            
##   [4] igraph_1.5.0.1         gmm_1.8                lazyeval_0.2.2        
##   [7] shinydashboard_0.7.2   crosstalk_1.2.0        BiocParallel_1.32.6   
##  [10] digest_0.6.33          foreach_1.5.2          htmltools_0.5.5       
##  [13] fansi_1.0.4            magrittr_2.0.3         memoise_2.0.1         
##  [16] cluster_2.1.4          doParallel_1.0.17      tzdb_0.4.0            
##  [19] limma_3.54.2           ComplexHeatmap_2.14.0  Biostrings_2.66.0     
##  [22] imputeLCMD_2.1         sandwich_3.0-2         timechange_0.2.0      
##  [25] colorspace_2.1-0       blob_1.2.4             xfun_0.39             
##  [28] crayon_1.5.2           RCurl_1.98-1.12        jsonlite_1.8.7        
##  [31] impute_1.72.3          zoo_1.8-12             iterators_1.0.14      
##  [34] glue_1.6.2             hash_2.2.6.2           gtable_0.3.3          
##  [37] zlibbioc_1.44.0        XVector_0.38.0         GetoptLong_1.0.5      
##  [40] DelayedArray_0.24.0    shape_1.4.6            scales_1.2.1          
##  [43] pheatmap_1.0.12        vsn_3.66.0             mvtnorm_1.2-2         
##  [46] DBI_1.1.3              Rcpp_1.0.11            plotrix_3.8-2         
##  [49] mzR_2.32.0             viridisLite_0.4.2      xtable_1.8-4          
##  [52] clue_0.3-64            reactome.db_1.82.0     bit_4.0.5             
##  [55] preprocessCore_1.60.2  sqldf_0.4-11           MsCoreUtils_1.10.0    
##  [58] DT_0.28                htmlwidgets_1.6.2      httr_1.4.6            
##  [61] gplots_3.1.3           RColorBrewer_1.1-3     ellipsis_0.3.2        
##  [64] farver_2.1.1           pkgconfig_2.0.3        XML_3.99-0.14         
##  [67] sass_0.4.7             utf8_1.2.3             STRINGdb_2.10.1       
##  [70] labeling_0.4.2         tidyselect_1.2.0       rlang_1.1.1           
##  [73] later_1.3.1            munsell_0.5.0          tools_4.2.3           
##  [76] cachem_1.0.8           cli_3.6.1              gsubfn_0.7            
##  [79] generics_0.1.3         RSQLite_2.3.1          fdrtool_1.2.17        
##  [82] evaluate_0.21          fastmap_1.1.1          mzID_1.36.0           
##  [85] yaml_2.3.7             knitr_1.43             bit64_4.0.5           
##  [88] caTools_1.18.2         KEGGREST_1.38.0        ncdf4_1.21            
##  [91] mime_0.12              compiler_4.2.3         rstudioapi_0.15.0     
##  [94] plotly_4.10.2          png_0.1-8              affyio_1.68.0         
##  [97] stringi_1.7.12         bslib_0.5.0            highr_0.10            
## [100] MSnbase_2.24.2         lattice_0.21-8         ProtGenerics_1.30.0   
## [103] Matrix_1.6-0           tmvtnorm_1.5           vctrs_0.6.3           
## [106] pillar_1.9.0           norm_1.0-11.1          lifecycle_1.0.3       
## [109] BiocManager_1.30.21.1  jquerylib_0.1.4        MALDIquant_1.22.1     
## [112] GlobalOptions_0.1.2    data.table_1.14.8      cowplot_1.1.1         
## [115] bitops_1.0-7           httpuv_1.6.11          R6_2.5.1              
## [118] pcaMethods_1.90.0      affy_1.76.0            promises_1.2.0.1      
## [121] KernSmooth_2.23-22     codetools_0.2-19       MASS_7.3-60           
## [124] gtools_3.9.4           assertthat_0.2.1       chron_2.3-61          
## [127] proto_1.0.0            rjson_0.2.21           withr_2.5.0           
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3         hms_1.1.3             
## [133] grid_4.2.3             rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()